Job Applicants recommendation engine.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TCS_Internship.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ak-ghoul/Deep-Learning/blob/master/Job%20Applicants%20recommendation%20engine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7MMd_6laMC10"
      },
      "source": [
        "# Blueprint of Recommender System\n",
        "\n",
        "###Step by step procedure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rpEP3g7HS41"
      },
      "source": [
        "# importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "vuvrUg8uM18V",
        "outputId": "c28a5247-8fa5-49a2-fdba-b8aede93e9dc"
      },
      "source": [
        "#uploading dataset I took from Kaggle containig Job Profiles\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-9de763d5-51b9-46ff-b98d-b7ef2d365754\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-9de763d5-51b9-46ff-b98d-b7ef2d365754\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Combined_Jobs_Final.csv to Combined_Jobs_Final.csv\n",
            "User uploaded file \"Combined_Jobs_Final.csv\" with length 28046958 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3eG0oDzvM4lN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "4d83c18a-81d2-4008-ae8b-5d831843112a"
      },
      "source": [
        "job_details = pd.read_csv('Combined_Jobs_Final.csv', engine='python', error_bad_lines=False)\n",
        "print(job_details.shape)\n",
        "job_details.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(14999, 23)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Job.ID</th>\n",
              "      <th>Provider</th>\n",
              "      <th>Status</th>\n",
              "      <th>Slug</th>\n",
              "      <th>Title</th>\n",
              "      <th>Position</th>\n",
              "      <th>Company</th>\n",
              "      <th>City</th>\n",
              "      <th>State.Name</th>\n",
              "      <th>State.Code</th>\n",
              "      <th>Address</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Industry</th>\n",
              "      <th>Job.Description</th>\n",
              "      <th>Requirements</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Listing.Start</th>\n",
              "      <th>Listing.End</th>\n",
              "      <th>Employment.Type</th>\n",
              "      <th>Education.Required</th>\n",
              "      <th>Created.At</th>\n",
              "      <th>Updated.At</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>111</td>\n",
              "      <td>1</td>\n",
              "      <td>open</td>\n",
              "      <td>palo-alto-ca-tacolicious-server</td>\n",
              "      <td>Server @ Tacolicious</td>\n",
              "      <td>Server</td>\n",
              "      <td>Tacolicious</td>\n",
              "      <td>Palo Alto</td>\n",
              "      <td>California</td>\n",
              "      <td>CA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.443346</td>\n",
              "      <td>-122.161170</td>\n",
              "      <td>Food and Beverages</td>\n",
              "      <td>Tacolicious' first Palo Alto store just opened...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Part-Time</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-03-12 02:08:28 UTC</td>\n",
              "      <td>2014-08-16 15:35:36 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>113</td>\n",
              "      <td>1</td>\n",
              "      <td>open</td>\n",
              "      <td>san-francisco-ca-claude-lane-kitchen-staff-chef</td>\n",
              "      <td>Kitchen Staff/Chef @ Claude Lane</td>\n",
              "      <td>Kitchen Staff/Chef</td>\n",
              "      <td>Claude Lane</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>CA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.789830</td>\n",
              "      <td>-122.404268</td>\n",
              "      <td>Food and Beverages</td>\n",
              "      <td>\\n\\nNew French Brasserie in S.F. Financial Di...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Part-Time</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-04-12 08:36:36 UTC</td>\n",
              "      <td>2014-08-16 15:35:36 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>117</td>\n",
              "      <td>1</td>\n",
              "      <td>open</td>\n",
              "      <td>san-francisco-ca-machka-restaurants-corp-barte...</td>\n",
              "      <td>Bartender @ Machka Restaurants Corp.</td>\n",
              "      <td>Bartender</td>\n",
              "      <td>Machka Restaurants Corp.</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>CA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.795597</td>\n",
              "      <td>-122.402963</td>\n",
              "      <td>Food and Beverages</td>\n",
              "      <td>We are a popular Mediterranean wine bar and re...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Part-Time</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-07-16 09:34:10 UTC</td>\n",
              "      <td>2014-08-16 15:35:37 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>121</td>\n",
              "      <td>1</td>\n",
              "      <td>open</td>\n",
              "      <td>brisbane-ca-teriyaki-house-server</td>\n",
              "      <td>Server @ Teriyaki House</td>\n",
              "      <td>Server</td>\n",
              "      <td>Teriyaki House</td>\n",
              "      <td>Brisbane</td>\n",
              "      <td>California</td>\n",
              "      <td>CA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.685073</td>\n",
              "      <td>-122.400275</td>\n",
              "      <td>Food and Beverages</td>\n",
              "      <td>? Serve food/drinks to customers in a profess...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Part-Time</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-09-04 15:40:30 UTC</td>\n",
              "      <td>2014-08-16 15:35:38 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>127</td>\n",
              "      <td>1</td>\n",
              "      <td>open</td>\n",
              "      <td>los-angeles-ca-rosa-mexicano-sunset-kitchen-st...</td>\n",
              "      <td>Kitchen Staff/Chef @ Rosa Mexicano - Sunset</td>\n",
              "      <td>Kitchen Staff/Chef</td>\n",
              "      <td>Rosa Mexicano - Sunset</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>CA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.073384</td>\n",
              "      <td>-118.460439</td>\n",
              "      <td>Food and Beverages</td>\n",
              "      <td>Located at the heart of Hollywood, we are one ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Part-Time</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-07-17 15:26:18 UTC</td>\n",
              "      <td>2014-08-16 15:35:40 UTC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Job.ID  Provider  ...               Created.At               Updated.At\n",
              "0     111         1  ...  2013-03-12 02:08:28 UTC  2014-08-16 15:35:36 UTC\n",
              "1     113         1  ...  2013-04-12 08:36:36 UTC  2014-08-16 15:35:36 UTC\n",
              "2     117         1  ...  2013-07-16 09:34:10 UTC  2014-08-16 15:35:37 UTC\n",
              "3     121         1  ...  2013-09-04 15:40:30 UTC  2014-08-16 15:35:38 UTC\n",
              "4     127         1  ...  2013-07-17 15:26:18 UTC  2014-08-16 15:35:40 UTC\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALXg2FPiQT6E"
      },
      "source": [
        "#Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRaPMfH3SnNs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "c75db172-125a-4781-a906-9ad7965641fc"
      },
      "source": [
        "# Data Cleaning \n",
        "columns_keep = ['Job.ID', 'Slug', 'Title', 'Position', 'Company', 'City', 'Employment.Type', 'Education.Required', 'Job.Description']\n",
        "job_details = job_details[columns_keep]\n",
        "job_details.head() "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Job.ID</th>\n",
              "      <th>Slug</th>\n",
              "      <th>Title</th>\n",
              "      <th>Position</th>\n",
              "      <th>Company</th>\n",
              "      <th>City</th>\n",
              "      <th>Employment.Type</th>\n",
              "      <th>Education.Required</th>\n",
              "      <th>Job.Description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>111</td>\n",
              "      <td>palo-alto-ca-tacolicious-server</td>\n",
              "      <td>Server @ Tacolicious</td>\n",
              "      <td>Server</td>\n",
              "      <td>Tacolicious</td>\n",
              "      <td>Palo Alto</td>\n",
              "      <td>Part-Time</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tacolicious' first Palo Alto store just opened...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>113</td>\n",
              "      <td>san-francisco-ca-claude-lane-kitchen-staff-chef</td>\n",
              "      <td>Kitchen Staff/Chef @ Claude Lane</td>\n",
              "      <td>Kitchen Staff/Chef</td>\n",
              "      <td>Claude Lane</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Part-Time</td>\n",
              "      <td>NaN</td>\n",
              "      <td>\\n\\nNew French Brasserie in S.F. Financial Di...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>117</td>\n",
              "      <td>san-francisco-ca-machka-restaurants-corp-barte...</td>\n",
              "      <td>Bartender @ Machka Restaurants Corp.</td>\n",
              "      <td>Bartender</td>\n",
              "      <td>Machka Restaurants Corp.</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Part-Time</td>\n",
              "      <td>NaN</td>\n",
              "      <td>We are a popular Mediterranean wine bar and re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>121</td>\n",
              "      <td>brisbane-ca-teriyaki-house-server</td>\n",
              "      <td>Server @ Teriyaki House</td>\n",
              "      <td>Server</td>\n",
              "      <td>Teriyaki House</td>\n",
              "      <td>Brisbane</td>\n",
              "      <td>Part-Time</td>\n",
              "      <td>NaN</td>\n",
              "      <td>? Serve food/drinks to customers in a profess...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>127</td>\n",
              "      <td>los-angeles-ca-rosa-mexicano-sunset-kitchen-st...</td>\n",
              "      <td>Kitchen Staff/Chef @ Rosa Mexicano - Sunset</td>\n",
              "      <td>Kitchen Staff/Chef</td>\n",
              "      <td>Rosa Mexicano - Sunset</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Part-Time</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Located at the heart of Hollywood, we are one ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Job.ID  ...                                    Job.Description\n",
              "0     111  ...  Tacolicious' first Palo Alto store just opened...\n",
              "1     113  ...   \\n\\nNew French Brasserie in S.F. Financial Di...\n",
              "2     117  ...  We are a popular Mediterranean wine bar and re...\n",
              "3     121  ...   ? Serve food/drinks to customers in a profess...\n",
              "4     127  ...  Located at the heart of Hollywood, we are one ...\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gz_lQqE7OupZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a65500f-c481-43d1-fc06-2f1b9239a73b"
      },
      "source": [
        "job_details.isnull().sum()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Job.ID                  0\n",
              "Slug                    0\n",
              "Title                   0\n",
              "Position                0\n",
              "Company               600\n",
              "City                   11\n",
              "Employment.Type        10\n",
              "Education.Required    100\n",
              "Job.Description         7\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cl665WwWVFCJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6808da1-e9d2-4f9f-dd08-aba2bdcdf61c"
      },
      "source": [
        "# As we can see, some columns have Not Assigned values.\n",
        "# Dropping the rows with Not Assigned 'Company, City & Job.Description'\n",
        "job_details.dropna(subset = [\"Company\"], inplace=True)\n",
        "job_details.dropna(subset = [\"City\"], inplace=True)\n",
        "job_details.dropna(subset = [\"Job.Description\"], inplace=True)\n",
        "\n",
        "#Few rows have Education not defined.\n",
        "#Replacing the NaN values of Education.Required type with \"Not Specified\" and Employment.Required as \"Full-Time/Part-Time\"\n",
        "job_details['Education.Required'] = job_details['Education.Required'].fillna(\"Not Specified\")\n",
        "job_details['Employment.Type'] = job_details['Employment.Type'].fillna(\"Full-Time/Part-Time\")\n",
        "job_details.isnull().sum()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Job.ID                0\n",
              "Slug                  0\n",
              "Title                 0\n",
              "Position              0\n",
              "Company               0\n",
              "City                  0\n",
              "Employment.Type       0\n",
              "Education.Required    0\n",
              "Job.Description       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvyEo9s8X0JV"
      },
      "source": [
        "## Corpus\n",
        "#### A collection of text in readable format used in NLP.\n",
        "####The details of job in dataset will be combined to form a readable corpus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ioPKRuDDnKS-"
      },
      "source": [
        "#Corpus is made to apply NLP easily\n",
        "final_jobs = job_details['Job.ID']\n",
        "final_jobs = pd.DataFrame(final_jobs)  # need to make it pandas dataframe before adding pandas columns into it.\n",
        "final_jobs['pos_company_city_emptype_jobdesc'] = job_details[\"Position\"]+\" \"+job_details[\"Company\"]+\" \"+job_details[\"City\"]+\" \"+job_details[\"Employment.Type\"]+\" \"+job_details[\"Job.Description\"]"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UlE8LPYvAW3F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "414632b3-14d9-474d-8cd3-99a1ea0104df"
      },
      "source": [
        "# cleaning the corpus \n",
        "# replacing unnecessary symbols with space\n",
        "final_jobs['pos_company_city_emptype_jobdesc'] = final_jobs['pos_company_city_emptype_jobdesc'].str.replace('[^a-zA-Z \\n\\.]',\" \")\n",
        "# converting all characters to lowercase\n",
        "final_jobs['pos_company_city_emptype_jobdesc'] = final_jobs['pos_company_city_emptype_jobdesc'].str.lower()\n",
        "final_jobs.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Job.ID</th>\n",
              "      <th>pos_company_city_emptype_jobdesc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>111</td>\n",
              "      <td>server tacolicious palo alto part time tacolic...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>113</td>\n",
              "      <td>kitchen staff chef claude lane san francisco p...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>117</td>\n",
              "      <td>bartender machka restaurants corp. san francis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>121</td>\n",
              "      <td>server teriyaki house brisbane part time    se...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>127</td>\n",
              "      <td>kitchen staff chef rosa mexicano   sunset los ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Job.ID                   pos_company_city_emptype_jobdesc\n",
              "0     111  server tacolicious palo alto part time tacolic...\n",
              "1     113  kitchen staff chef claude lane san francisco p...\n",
              "2     117  bartender machka restaurants corp. san francis...\n",
              "3     121  server teriyaki house brisbane part time    se...\n",
              "4     127  kitchen staff chef rosa mexicano   sunset los ..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5XkV8fJQ9RD5"
      },
      "source": [
        "#NLPTK(Natural Language Processing Toolkit)\n",
        "### --> Stop Words and Porter Stemming"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCxP3odN9la4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "3be6a263-26c5-4489-9817-057111225e98"
      },
      "source": [
        "from nltk.stem import PorterStemmer\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "# applying stop words\n",
        "stop = stopwords.words('english')\n",
        "final_jobs['pos_company_city_emptype_jobdesc'].apply(lambda x: ' '.join([word for word in x.split() if word not in stop]))\n",
        "\n",
        "# applying stemmer - This basically removes all duplicate words or words having same meaning. It gives root forms of words. \n",
        "stemmer = PorterStemmer()\n",
        "final_jobs['pos_company_city_emptype_jobdesc'].apply(lambda x: ' '.join(stemmer.stem(word) for word in x))\n",
        "final_jobs.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Job.ID</th>\n",
              "      <th>pos_company_city_emptype_jobdesc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>111</td>\n",
              "      <td>server tacolicious palo alto part time tacolic...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>113</td>\n",
              "      <td>kitchen staff chef claude lane san francisco p...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>117</td>\n",
              "      <td>bartender machka restaurants corp. san francis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>121</td>\n",
              "      <td>server teriyaki house brisbane part time    se...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>127</td>\n",
              "      <td>kitchen staff chef rosa mexicano   sunset los ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Job.ID                   pos_company_city_emptype_jobdesc\n",
              "0     111  server tacolicious palo alto part time tacolic...\n",
              "1     113  kitchen staff chef claude lane san francisco p...\n",
              "2     117  bartender machka restaurants corp. san francis...\n",
              "3     121  server teriyaki house brisbane part time    se...\n",
              "4     127  kitchen staff chef rosa mexicano   sunset los ..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oMFAb8VcGGGW"
      },
      "source": [
        "## Dealing with data of Users who applied for Jobs\n",
        "##### We have 3 different datasets of users, \n",
        "##### -> Experience\n",
        "##### -> Jobs Viewed\n",
        "##### -> Positions of Interest\n",
        "##### I will combine these datasets and  use it in Cosine Similarity for recommending these users to job posts."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iM166qZo6Dco",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "cf7b7d01-979c-4ca6-c38f-2c7d87a476f9"
      },
      "source": [
        "#Uploading the Experience Dataset\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-b9ecc1fc-27c7-4797-a48d-dcc8a442de61\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-b9ecc1fc-27c7-4797-a48d-dcc8a442de61\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Experience.csv to Experience.csv\n",
            "User uploaded file \"Experience.csv\" with length 2836104 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHSA8sAgDGWw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "5ce494e4-a12a-4686-eaca-ed87ec4fdc35"
      },
      "source": [
        "experience = pd.read_csv('Experience.csv')\n",
        "experience.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Applicant.ID</th>\n",
              "      <th>Position.Name</th>\n",
              "      <th>Employer.Name</th>\n",
              "      <th>City</th>\n",
              "      <th>State.Name</th>\n",
              "      <th>State.Code</th>\n",
              "      <th>Start.Date</th>\n",
              "      <th>End.Date</th>\n",
              "      <th>Job.Description</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Can.Contact.Employer</th>\n",
              "      <th>Created.At</th>\n",
              "      <th>Updated.At</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10001</td>\n",
              "      <td>Account Manager / Sales Administration / Quali...</td>\n",
              "      <td>Barcode Resourcing</td>\n",
              "      <td>Bellingham</td>\n",
              "      <td>Washington</td>\n",
              "      <td>WA</td>\n",
              "      <td>2012-10-15</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-12-12 20:10:02 UTC</td>\n",
              "      <td>2014-12-12 20:10:02 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10001</td>\n",
              "      <td>Electronics Technician / Item Master Controller</td>\n",
              "      <td>Ryzex Group</td>\n",
              "      <td>Bellingham</td>\n",
              "      <td>Washington</td>\n",
              "      <td>WA</td>\n",
              "      <td>2001-12-01</td>\n",
              "      <td>2012-04-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-12-12 20:10:02 UTC</td>\n",
              "      <td>2014-12-12 20:10:02 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10001</td>\n",
              "      <td>Machine Operator</td>\n",
              "      <td>comptec inc</td>\n",
              "      <td>Custer</td>\n",
              "      <td>Washington</td>\n",
              "      <td>WA</td>\n",
              "      <td>1997-01-01</td>\n",
              "      <td>1999-01-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-12-12 20:10:02 UTC</td>\n",
              "      <td>2014-12-12 20:10:02 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10003</td>\n",
              "      <td>maintenance technician</td>\n",
              "      <td>Winn residental</td>\n",
              "      <td>washington</td>\n",
              "      <td>District of Columbia</td>\n",
              "      <td>DC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Necessary maintenance for \"Make Ready\" Plumbin...</td>\n",
              "      <td>10.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2014-12-12 21:27:05 UTC</td>\n",
              "      <td>2014-12-12 21:27:05 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10003</td>\n",
              "      <td>Electrical Helper</td>\n",
              "      <td>michael and son services</td>\n",
              "      <td>alexandria</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>VA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>repair and services of electrical construction</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>2014-12-12 21:27:05 UTC</td>\n",
              "      <td>2014-12-12 21:27:05 UTC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Applicant.ID  ...               Updated.At\n",
              "0         10001  ...  2014-12-12 20:10:02 UTC\n",
              "1         10001  ...  2014-12-12 20:10:02 UTC\n",
              "2         10001  ...  2014-12-12 20:10:02 UTC\n",
              "3         10003  ...  2014-12-12 21:27:05 UTC\n",
              "4         10003  ...  2014-12-12 21:27:05 UTC\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86jKD0rP7HO1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a069e3f4-ebce-47fd-a90a-2b9d3b778f14"
      },
      "source": [
        "# making this dataset ready for use. Repeating the steps of data cleaning\n",
        "experience_final = experience['Applicant.ID']\n",
        "experience_final = pd.DataFrame(experience_final)\n",
        "experience_final['pos_empname'] = experience['Position.Name'] +\" \"+ experience['Employer.Name'] # these are the only details we require\n",
        "# removing null rows\n",
        "experience_final.dropna()\n",
        "\n",
        "experience_final['pos_empname'] = experience_final['pos_empname'].str.replace('[^a-zA-Z \\n\\.]',\" \")\n",
        "# converting all characters to lowercase\n",
        "experience_final['pos_empname'] = experience_final['pos_empname'].str.lower()\n",
        "experience_final = experience_final.sort_values(by='Applicant.ID')\n",
        "experience_final.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Applicant.ID</th>\n",
              "      <th>pos_empname</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2763</th>\n",
              "      <td>2</td>\n",
              "      <td>volunteer school for self healing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2762</th>\n",
              "      <td>2</td>\n",
              "      <td>writer for the uloop blog cecilia abate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3759</th>\n",
              "      <td>3</td>\n",
              "      <td>marketing intern honda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3758</th>\n",
              "      <td>3</td>\n",
              "      <td>server aloha beach resort</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3757</th>\n",
              "      <td>3</td>\n",
              "      <td>prep cook moscone center</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Applicant.ID                              pos_empname\n",
              "2763             2        volunteer school for self healing\n",
              "2762             2  writer for the uloop blog cecilia abate\n",
              "3759             3                   marketing intern honda\n",
              "3758             3                server aloha beach resort\n",
              "3757             3                 prep cook moscone center"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8uAhlyYS8NxJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "307e869c-e07b-44ef-eb4c-9cfa9fe2653b"
      },
      "source": [
        "# As we can see, same id have more than one experience, so clubbing them together..\n",
        "experience_final = experience_final.groupby('Applicant.ID', sort=False).sum().reset_index()\n",
        "experience_final.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Applicant.ID</th>\n",
              "      <th>pos_empname</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>volunteer school for self healingwriter for th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>marketing intern hondaserver aloha beach resor...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>project assistant iom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>deli clerk server  cashier  food prep  order t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>cashier cristina green</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Applicant.ID                                        pos_empname\n",
              "0             2  volunteer school for self healingwriter for th...\n",
              "1             3  marketing intern hondaserver aloha beach resor...\n",
              "2             6                              project assistant iom\n",
              "3             8  deli clerk server  cashier  food prep  order t...\n",
              "4            11                             cashier cristina green"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "RXXfTNgtSmF8",
        "outputId": "e9dc8ef2-1934-429e-eb8a-117712ddfb71"
      },
      "source": [
        "# Uploading Jobs viewed by candidates Dataset\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-67ac7bb7-642a-4228-aa8a-56b0a448e4c5\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-67ac7bb7-642a-4228-aa8a-56b0a448e4c5\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Job_Views.csv to Job_Views.csv\n",
            "User uploaded file \"Job_Views.csv\" with length 3291093 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_s-8Vv-Sr-S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "74aef1c7-406a-4ab7-b4d1-e830cc631e74"
      },
      "source": [
        "job_views = pd.read_csv('Job_Views.csv')\n",
        "job_views.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Applicant.ID</th>\n",
              "      <th>Job.ID</th>\n",
              "      <th>Title</th>\n",
              "      <th>Position</th>\n",
              "      <th>Company</th>\n",
              "      <th>City</th>\n",
              "      <th>State.Name</th>\n",
              "      <th>State.Code</th>\n",
              "      <th>Industry</th>\n",
              "      <th>View.Start</th>\n",
              "      <th>View.End</th>\n",
              "      <th>View.Duration</th>\n",
              "      <th>Created.At</th>\n",
              "      <th>Updated.At</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000</td>\n",
              "      <td>73666</td>\n",
              "      <td>Cashiers &amp; Valets Needed! @ WallyPark</td>\n",
              "      <td>Cashiers &amp; Valets Needed!</td>\n",
              "      <td>WallyPark</td>\n",
              "      <td>Newark</td>\n",
              "      <td>New Jersey</td>\n",
              "      <td>NJ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-12-12 20:12:35 UTC</td>\n",
              "      <td>2014-12-12 20:31:24 UTC</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>2014-12-12 20:12:35 UTC</td>\n",
              "      <td>2014-12-12 20:12:35 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10000</td>\n",
              "      <td>96655</td>\n",
              "      <td>Macy's Seasonal Retail Fragrance Cashier  - Ga...</td>\n",
              "      <td>Macy's Seasonal Retail Fragrance Cashier  - Ga...</td>\n",
              "      <td>Macy's</td>\n",
              "      <td>Garden City</td>\n",
              "      <td>New York</td>\n",
              "      <td>NY</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-12-12 20:08:50 UTC</td>\n",
              "      <td>2014-12-12 20:10:15 UTC</td>\n",
              "      <td>84.0</td>\n",
              "      <td>2014-12-12 20:08:50 UTC</td>\n",
              "      <td>2014-12-12 20:08:50 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10001</td>\n",
              "      <td>84141</td>\n",
              "      <td>Part Time Showroom Sales / Cashier @ Grizzly I...</td>\n",
              "      <td>Part Time Showroom Sales / Cashier</td>\n",
              "      <td>Grizzly Industrial Inc.</td>\n",
              "      <td>Bellingham</td>\n",
              "      <td>Washington</td>\n",
              "      <td>WA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-12-12 20:12:32 UTC</td>\n",
              "      <td>2014-12-12 20:17:18 UTC</td>\n",
              "      <td>286.0</td>\n",
              "      <td>2014-12-12 20:12:32 UTC</td>\n",
              "      <td>2014-12-12 20:12:32 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10002</td>\n",
              "      <td>77989</td>\n",
              "      <td>Event Specialist Part Time @ Advantage Sales &amp;...</td>\n",
              "      <td>Event Specialist Part Time</td>\n",
              "      <td>Advantage Sales &amp; Marketing</td>\n",
              "      <td>Simpsonville</td>\n",
              "      <td>South Carolina</td>\n",
              "      <td>SC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-12-12 20:39:23 UTC</td>\n",
              "      <td>2014-12-12 20:42:13 UTC</td>\n",
              "      <td>170.0</td>\n",
              "      <td>2014-12-12 20:39:23 UTC</td>\n",
              "      <td>2014-12-12 20:39:23 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10002</td>\n",
              "      <td>69568</td>\n",
              "      <td>Bonefish - Kitchen Staff @ Bonefish Grill</td>\n",
              "      <td>Bonefish - Kitchen Staff</td>\n",
              "      <td>Bonefish Grill</td>\n",
              "      <td>Greenville</td>\n",
              "      <td>South Carolina</td>\n",
              "      <td>SC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-12-12 20:43:25 UTC</td>\n",
              "      <td>2014-12-12 20:43:58 UTC</td>\n",
              "      <td>33.0</td>\n",
              "      <td>2014-12-12 20:43:25 UTC</td>\n",
              "      <td>2014-12-12 20:43:25 UTC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Applicant.ID  Job.ID  ...               Created.At               Updated.At\n",
              "0         10000   73666  ...  2014-12-12 20:12:35 UTC  2014-12-12 20:12:35 UTC\n",
              "1         10000   96655  ...  2014-12-12 20:08:50 UTC  2014-12-12 20:08:50 UTC\n",
              "2         10001   84141  ...  2014-12-12 20:12:32 UTC  2014-12-12 20:12:32 UTC\n",
              "3         10002   77989  ...  2014-12-12 20:39:23 UTC  2014-12-12 20:39:23 UTC\n",
              "4         10002   69568  ...  2014-12-12 20:43:25 UTC  2014-12-12 20:43:25 UTC\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5MkgpYIS3pb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "48f08160-0d6d-4264-b8c6-2b79390dd11f"
      },
      "source": [
        "#Extracting wanted columns\n",
        "job_views_final = job_views['Applicant.ID']\n",
        "job_views_final = pd.DataFrame(job_views_final)\n",
        "job_views_final['pos_com_city'] = job_views['Position'] +\" \"+ job_views['Company'] +\" \"+ job_views['City']\n",
        "\n",
        "#Cleaning up & converting to lowercase\n",
        "job_views_final['pos_com_city'] = job_views_final['pos_com_city'].str.replace(\"[^a-zA-Z \\n\\.]\", \" \")\n",
        "job_views_final['pos_com_city'] = job_views_final['pos_com_city'].str.lower()\n",
        "job_views_final.sort_values(by=\"Applicant.ID\")\n",
        "job_views_final.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Applicant.ID</th>\n",
              "      <th>pos_com_city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000</td>\n",
              "      <td>cashiers   valets needed  wallypark newark</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10000</td>\n",
              "      <td>macy s seasonal retail fragrance cashier    ga...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10001</td>\n",
              "      <td>part time showroom sales   cashier grizzly ind...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10002</td>\n",
              "      <td>event specialist part time advantage sales   m...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10002</td>\n",
              "      <td>bonefish   kitchen staff bonefish grill greenv...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Applicant.ID                                       pos_com_city\n",
              "0         10000         cashiers   valets needed  wallypark newark\n",
              "1         10000  macy s seasonal retail fragrance cashier    ga...\n",
              "2         10001  part time showroom sales   cashier grizzly ind...\n",
              "3         10002  event specialist part time advantage sales   m...\n",
              "4         10002  bonefish   kitchen staff bonefish grill greenv..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W13IuLFnZ5To",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "85eab9e0-c0c2-429b-e099-ca2e8c6cbe42"
      },
      "source": [
        "# opening Positions of Interest Dataset\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f4620498-c466-4e03-9a2f-d47f6a9e0fde\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-f4620498-c466-4e03-9a2f-d47f6a9e0fde\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Positions_Of_Interest.csv to Positions_Of_Interest.csv\n",
            "User uploaded file \"Positions_Of_Interest.csv\" with length 478462 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWYK3jaSaAwH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "04b95a23-c6fa-4862-9ede-a3a2f59ce774"
      },
      "source": [
        "posof_interest = pd.read_csv(\"Positions_Of_Interest.csv\")\n",
        "posof_interest.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Applicant.ID</th>\n",
              "      <th>Position.Of.Interest</th>\n",
              "      <th>Created.At</th>\n",
              "      <th>Updated.At</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10003</td>\n",
              "      <td>security officer</td>\n",
              "      <td>2014-12-12 21:20:54 UTC</td>\n",
              "      <td>2014-12-12 21:20:54 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10007</td>\n",
              "      <td>Server</td>\n",
              "      <td>2014-08-14 15:56:42 UTC</td>\n",
              "      <td>2015-02-26 20:35:12 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10007</td>\n",
              "      <td>Bartender</td>\n",
              "      <td>2014-08-14 15:56:44 UTC</td>\n",
              "      <td>2015-02-19 23:21:28 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10008</td>\n",
              "      <td>Host</td>\n",
              "      <td>2014-08-14 15:56:42 UTC</td>\n",
              "      <td>2015-02-26 20:35:12 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10008</td>\n",
              "      <td>Barista</td>\n",
              "      <td>2014-08-14 15:56:43 UTC</td>\n",
              "      <td>2015-02-18 02:35:06 UTC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Applicant.ID  ...               Updated.At\n",
              "0         10003  ...  2014-12-12 21:20:54 UTC\n",
              "1         10007  ...  2015-02-26 20:35:12 UTC\n",
              "2         10007  ...  2015-02-19 23:21:28 UTC\n",
              "3         10008  ...  2015-02-26 20:35:12 UTC\n",
              "4         10008  ...  2015-02-18 02:35:06 UTC\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Dr9suCmaPvi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9367adbd-cfbe-4712-b7b2-b7c531676ab5"
      },
      "source": [
        "posof_interest = posof_interest.drop(\"Created.At\", axis=1)\n",
        "posof_interest = posof_interest.drop(\"Updated.At\", axis=1)\n",
        "\n",
        "#cleaning and converting to lower\n",
        "posof_interest['Position.Of.Interest'] = posof_interest['Position.Of.Interest'].str.replace(\"[^a-zA-Z \\n\\.]\", \" \")\n",
        "posof_interest['Position.Of.Interest'] = posof_interest['Position.Of.Interest'].str.lower()\n",
        "posof_interest.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Applicant.ID</th>\n",
              "      <th>Position.Of.Interest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10003</td>\n",
              "      <td>security officer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10007</td>\n",
              "      <td>server</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10007</td>\n",
              "      <td>bartender</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10008</td>\n",
              "      <td>host</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10008</td>\n",
              "      <td>barista</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Applicant.ID Position.Of.Interest\n",
              "0         10003     security officer\n",
              "1         10007               server\n",
              "2         10007            bartender\n",
              "3         10008                 host\n",
              "4         10008              barista"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0yqHHTbzUnrS"
      },
      "source": [
        "### Merging the Datasets to make one Ultimate Dataset of Applicants\n",
        "##### This will be used for comparing with the Job Details Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "biDLWFdNodtb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "66c1784c-e4ed-4ee7-f769-86fbc76964e9"
      },
      "source": [
        "# combining Experience & Job Views Datasets\n",
        "outer_join = pd.merge(experience_final, job_views_final, on=\"Applicant.ID\", how='outer')\n",
        "outer_join = outer_join.sort_values(by=\"Applicant.ID\")\n",
        "outer_join = outer_join.fillna(' ')\n",
        "print(outer_join.shape)\n",
        "outer_join.head()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(15383, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Applicant.ID</th>\n",
              "      <th>pos_empname</th>\n",
              "      <th>pos_com_city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>volunteer school for self healingwriter for th...</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>marketing intern hondaserver aloha beach resor...</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>project assistant iom</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>deli clerk server  cashier  food prep  order t...</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>cashier cristina green</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Applicant.ID                                        pos_empname pos_com_city\n",
              "0             2  volunteer school for self healingwriter for th...             \n",
              "1             3  marketing intern hondaserver aloha beach resor...             \n",
              "2             6                              project assistant iom             \n",
              "3             8  deli clerk server  cashier  food prep  order t...             \n",
              "4            11                             cashier cristina green             "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRyXd9C4y4ty",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "4b435bc5-cf08-434b-c51b-4058516fe9fc"
      },
      "source": [
        "# Combining the resultant dataset with Positions Of Interest Dataset\n",
        "candidate = pd.merge(outer_join, posof_interest, on = 'Applicant.ID', how= 'outer')\n",
        "candidate = candidate.sort_values(by = \"Applicant.ID\")\n",
        "candidate = candidate.fillna(' ')\n",
        "candidate.head()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Applicant.ID</th>\n",
              "      <th>pos_empname</th>\n",
              "      <th>pos_com_city</th>\n",
              "      <th>Position.Of.Interest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>volunteer school for self healingwriter for th...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>marketing intern hondaserver aloha beach resor...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>project assistant iom</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>deli clerk server  cashier  food prep  order t...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>cashier cristina green</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Applicant.ID  ... Position.Of.Interest\n",
              "0             2  ...                     \n",
              "1             3  ...                     \n",
              "2             6  ...                     \n",
              "3             8  ...                     \n",
              "4            11  ...                     \n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQaoeGm7zyT0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "709cf6fd-82be-49f1-9dab-004abfc21375"
      },
      "source": [
        "# Combining all these columns to make one single column just like it is present in Jobs Dataset\n",
        "applicant_final = candidate[\"Applicant.ID\"]\n",
        "applicant_final = pd.DataFrame(applicant_final)\n",
        "applicant_final[\"experience_jobview_posofinterest\"] = candidate[\"pos_empname\"].map(str)+\" \"+candidate[\"pos_com_city\"]+\" \"+candidate[\"Position.Of.Interest\"]\n",
        "applicant_final.head()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Applicant.ID</th>\n",
              "      <th>experience_jobview_posofinterest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>volunteer school for self healingwriter for th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>marketing intern hondaserver aloha beach resor...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>project assistant iom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>deli clerk server  cashier  food prep  order t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>cashier cristina green</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Applicant.ID                   experience_jobview_posofinterest\n",
              "0             2  volunteer school for self healingwriter for th...\n",
              "1             3  marketing intern hondaserver aloha beach resor...\n",
              "2             6                          project assistant iom    \n",
              "3             8  deli clerk server  cashier  food prep  order t...\n",
              "4            11                         cashier cristina green    "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ee4jy4-RfFpj"
      },
      "source": [
        "# TF-IDF (Term Frequency - Inverse Document Frequency)\n",
        "\n",
        "##### TF - The count of how many times a word has occured in the document.\n",
        "##### IDF - The importance of a word, more it occurs, less is it's importance.\n",
        "\n",
        "###### I will use the tf-idf vectorizer from Scikit Learn library.\n",
        "\n",
        "###### I will apply tf-idf on Final Applicants Dataset and on one job id because applicants will be recommended for this one job id."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjtRPCV0cVBM"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer    #importing tf-idf vectorizer\n",
        "tf_idf = TfidfVectorizer(use_idf = True)\n",
        "\n",
        "tf_idf_applicants = tf_idf.fit_transform(applicant_final['experience_jobview_posofinterest'])\n",
        "# pandas_tfidf_applicants = pd.DataFrame(tf_idf_applicants[0].T.todense(), index=tf_idf.get_feature_names(), columns=[\"Tf-idf\"])\n",
        "# pandas_tfidf_applicants = pandas_tfidf_applicants.sort_values(\"Tf-idf\", ascending=False)\n",
        "# pandas_tfidf_applicants.head(20)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWvBzUywYPzD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "9c974967-faca-4bdb-817a-73ba4c5e6de0"
      },
      "source": [
        "# taking any one job ID\n",
        "job_id = 111\n",
        "one_job = final_jobs[final_jobs[\"Job.ID\"] == job_id]\n",
        "one_job"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Job.ID</th>\n",
              "      <th>pos_company_city_emptype_jobdesc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>111</td>\n",
              "      <td>server tacolicious palo alto part time tacolic...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Job.ID                   pos_company_city_emptype_jobdesc\n",
              "0     111  server tacolicious palo alto part time tacolic..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yKEG-m1g7qdA",
        "outputId": "b1d8ad3d-d1d9-41a1-973f-8e7bc1629f06"
      },
      "source": [
        "tf_idf_job = tf_idf.transform(one_job['pos_company_city_emptype_jobdesc'])\n",
        "print(tf_idf_job)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  (0, 14235)\t0.35686175741777343\n",
            "  (0, 14108)\t0.13951182973299198\n",
            "  (0, 13984)\t0.19942225032250227\n",
            "  (0, 13838)\t0.17548549548486653\n",
            "  (0, 13413)\t0.13204348077902364\n",
            "  (0, 13079)\t0.17548549548486653\n",
            "  (0, 12980)\t0.11344362259846906\n",
            "  (0, 12940)\t0.05345950020161394\n",
            "  (0, 12858)\t0.13660873010047558\n",
            "  (0, 12610)\t0.28160088348191686\n",
            "  (0, 12316)\t0.09921681840112731\n",
            "  (0, 12158)\t0.10439695399346109\n",
            "  (0, 12046)\t0.1458579673361562\n",
            "  (0, 11570)\t0.06967196563562301\n",
            "  (0, 11569)\t0.1861781694031969\n",
            "  (0, 10874)\t0.10433095334108843\n",
            "  (0, 10307)\t0.11013771603977987\n",
            "  (0, 9672)\t0.05380277978446565\n",
            "  (0, 9603)\t0.22862455980469545\n",
            "  (0, 9497)\t0.17548549548486653\n",
            "  (0, 9030)\t0.07708389413364936\n",
            "  (0, 8974)\t0.10598151883817078\n",
            "  (0, 7843)\t0.3170632852352687\n",
            "  (0, 7020)\t0.19942225032250227\n",
            "  (0, 6411)\t0.08996730377214683\n",
            "  (0, 6350)\t0.19167495962819206\n",
            "  (0, 6023)\t0.09649601492490693\n",
            "  (0, 4931)\t0.10293379380620389\n",
            "  (0, 4816)\t0.12320405456235023\n",
            "  (0, 3801)\t0.13869863595097065\n",
            "  (0, 2565)\t0.1294219556448146\n",
            "  (0, 2114)\t0.07477543722433894\n",
            "  (0, 874)\t0.08520686603026441\n",
            "  (0, 774)\t0.13830465972792547\n",
            "  (0, 701)\t0.19167495962819206\n",
            "  (0, 553)\t0.06316331375343068\n",
            "  (0, 445)\t0.22862455980469545\n",
            "  (0, 106)\t0.17843087870888671\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2XttMFbA4MJ"
      },
      "source": [
        "# Cosine Similarity\n",
        "\n",
        "##### I have tf-idf vectors of both things, applicant dataset & a Job. \n",
        "##### I will apply Cosine Similarity on both of them to finally find the similarity and recommend applicants to a recruiter for a job."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74b9DeXvAsYW"
      },
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "# Applying Cosine-Sine similarity\n",
        "output = list(map(lambda x: cosine_similarity(tf_idf_job, x), tf_idf_applicants))\n",
        "# print(output[0])\n",
        "\n",
        "#getting applicant's id\n",
        "output_order = sorted(range(len(output)), key=lambda x: output[x].all(), reverse=True)[:30]\n",
        "count = 0\n",
        "recommendation = pd.DataFrame(columns = [\"Job.ID\", \"Recommended Applicants ID\"])\n",
        "for i in output_order:\n",
        "    recommendation.at[count, 'Job.ID'] = job_id\n",
        "    recommendation.at[count,'Recommended Applicants ID'] = applicant_final['Applicant.ID'][i]\n",
        "    count += 1"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7MXdn_pOEhIv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        },
        "outputId": "06a44c37-8e63-4339-8f95-ff942a0b20a9"
      },
      "source": [
        "recommendation    # Hence, Applicants are recommended to a given Job position"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Job.ID</th>\n",
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              "</div>"
            ],
            "text/plain": [
              "   Job.ID Recommended Applicants ID\n",
              "0     111                         2\n",
              "1     111                         8\n",
              "2     111                        11\n",
              "3     111                        12\n",
              "4     111                        13\n",
              "5     111                        18\n",
              "6     111                        21\n",
              "7     111                        22\n",
              "8     111                        23\n",
              "9     111                        24\n",
              "10    111                        26\n",
              "11    111                        27\n",
              "12    111                        30\n",
              "13    111                        33\n",
              "14    111                        37\n",
              "15    111                        40\n",
              "16    111                        42\n",
              "17    111                        42\n",
              "18    111                        48\n",
              "19    111                        52\n",
              "20    111                        63\n",
              "21    111                        68\n",
              "22    111                        69\n",
              "23    111                        73\n",
              "24    111                        74\n",
              "25    111                        75\n",
              "26    111                        83\n",
              "27    111                        84\n",
              "28    111                        96\n",
              "29    111                        96"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZRjJhvhV9zE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}